import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD

(x_train, y_train), (x_test, y_test) = mnist.load_data()
print('x_shape:', x_train.shape)  # (60000, 28, 28)
print('y_shape:', y_train.shape)  # (60000,)

# (60000,28,28)->(60000,784)
x_train = x_train.reshape(x_train.shape[0], -1) / 255.0  # -1自动判断大小，/255归一化
x_test = x_test.reshape(x_test.shape[0], -1) / 255.0

# 换one hot形式
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)

# 创建模型,输入784神经元，输出10个神经元
model = Sequential([
    Dense(units=10, input_dim=784, bias_initializer='one', activation='softmax')
])

# 定义优化器
sgd = SGD(lr=0.2)
model.compile(
    optimizer=sgd,
    loss='mse',
    metrics=['accuracy'],  # 计算准确率
)

# 训练模型
model.fit(x_train, y_train, batch_size=32, epochs=10)

# 评估模型
loss, accuracy = model.evaluate(x_test, y_test)

print('\ntest loss:', loss)
print('accuracy:', accuracy)

model.save('model-MNISClassification.h5')  # 保存模型,可添加路径

# 只保留参数，载入参数
# model.save_weights('model_weight.h5')
# model.load_weights('model_weight.h5')`

# 保存网络结构，载入网络结构
# from keras.models import model_from_json
# json_string = model.to_json()
# model = model_from_json(json_string)
